51
|
Mei H, Feng G, Zhu J, Lin S, Qiu Y, Wang Y, Xia T. A Practical Guide for Exploring Opportunities of Repurposing Drugs for CNS Diseases in Systems Biology. Methods Mol Biol 2016; 1303:531-547. [PMID: 26235090 DOI: 10.1007/978-1-4939-2627-5_33] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Systems biology has shown its potential in facilitating pathway-focused therapy development for central nervous system (CNS) diseases. An integrated network can be utilized to explore the multiple disease mechanisms and to discover repositioning opportunities. This review covers current therapeutic gaps for CNS diseases and the role of systems biology in pharmaceutical industry. We conclude with a Multiple Level Network Modeling (MLNM) example to illustrate the great potential of systems biology for CNS diseases. The system focuses on the benefit and practical applications in pathway centric therapy and drug repositioning.
Collapse
Affiliation(s)
- Hongkang Mei
- Informatics and Structure Biology, R&D China, GlaxoSmithKline, 917 Halei Road, Shanghai, 201203, China
| | | | | | | | | | | | | |
Collapse
|
52
|
Abstract
Enzymes are one of the most important groups of drug targets, and identifying possible ligand-enzyme interactions is of major importance in many drug discovery processes. Novel computational methods have been developed that can apply the information from the increasing number of resolved and available ligand-enzyme complexes to model new unknown interactions and therefore contribute to answer open questions in the field of drug discovery like the identification of unknown protein functions, off-target binding, ligand 3D homology modeling and induced-fit simulations.
Collapse
|
53
|
Chen XW, Duan W, Zhou SF. Repurposing paclitaxel for the treatment of fibrosis: indication discovery for existing drugs. DRUG DESIGN DEVELOPMENT AND THERAPY 2015; 9:4869-71. [PMID: 26379422 PMCID: PMC4567211 DOI: 10.2147/dddt.s87771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Xiao-Wu Chen
- Department of General Surgery, The First People's Hospital of Shunde, Southern Medical University, Shunde, Foshan, Guangdong, People's Republic of China
| | - Wei Duan
- School of Medicine, Deakin University, Waurn Ponds, Victoria, Australia
| | - Shu-Feng Zhou
- Department of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa, FL, USA
| |
Collapse
|
54
|
Bartolowits M, Davisson VJ. Considerations of Protein Subpockets in Fragment-Based Drug Design. Chem Biol Drug Des 2015; 87:5-20. [PMID: 26307335 DOI: 10.1111/cbdd.12631] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
While the fragment-based drug design approach continues to gain importance, gaps in the tools and methods available in the identification and accurate utilization of protein subpockets have limited the scope. The importance of these features of small molecule-protein recognition is highlighted with several examples. A generalized solution for the identification of subpockets and corresponding chemical fragments remains elusive, but there are numerous advancements in methods that can be used in combination to address subpockets. Finally, additional examples of approaches that consider the relative importance of small-molecule co-dependence of protein conformations are highlighted to emphasize an increased significance of subpockets, especially at protein interfaces.
Collapse
Affiliation(s)
- Matthew Bartolowits
- Department of Medicinal Chemistry and Molecular Pharmacology, College of Pharmacy, Purdue University, 575 Stadium Mall Dr., West Lafayette, IN, 47907, USA
| | - V Jo Davisson
- Department of Medicinal Chemistry and Molecular Pharmacology, College of Pharmacy, Purdue University, 575 Stadium Mall Dr., West Lafayette, IN, 47907, USA
| |
Collapse
|
55
|
Moreno-Ulloa A, Mendez-Luna D, Beltran-Partida E, Castillo C, Guevara G, Ramirez-Sanchez I, Correa-Basurto J, Ceballos G, Villarreal F. The effects of (-)-epicatechin on endothelial cells involve the G protein-coupled estrogen receptor (GPER). Pharmacol Res 2015; 100:309-20. [PMID: 26303816 DOI: 10.1016/j.phrs.2015.08.014] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2015] [Revised: 08/17/2015] [Accepted: 08/18/2015] [Indexed: 02/05/2023]
Abstract
We have provided evidence that the stimulatory effects of (-)-epicatechin ((-)-EPI) on endothelial cell nitric oxide (NO) production may involve the participation of a cell-surface receptor. Thus far, such entity(ies) has not been fully elucidated. The G protein-coupled estrogen receptor (GPER) is a cell-surface receptor that has been linked to protective effects on the cardiovascular system and activation of intracellular signaling pathways (including NO production) similar to those reported with (-)-EPI. In bovine coronary artery endothelial cells (BCAEC) by the use of confocal imaging, we evidence the presence of GPER at the cell-surface and on F-actin filaments. Using in silico studies we document the favorable binding mode between (-)-EPI and GPER. Such binding is comparable to that of the GPER agonist, G1. By the use of selective blockers, we demonstrate that the activation of ERK 1/2 and CaMKII by (-)-EPI is dependent on the GPER/c-SRC/EGFR axis mimicking those effects noted with G1. We also evidence by the use of siRNA the role that GPER has on mediating ERK1/2 activation by (-)-EPI. GPER appears to be coupled to a non Gαi/o or Gαs, protein subtype. To extrapolate our findings to an ex vivo model, we employed phenylephrine pre-contracted aortic rings evidencing that (-)-EPI can mediate vasodilation through GPER activation. In conclusion, we provide evidence that suggests the GPER as a potential mediator of (-)-EPI effects and highlights the important role that GPER may have on cardiovascular system protection.
Collapse
Affiliation(s)
- Aldo Moreno-Ulloa
- University of California, San Diego, School of Medicine, 9500 Gilman Drive, La Jolla, CA, USA; Laboratorio de Investigación Integral Cardiometabólica, Sección de Estudios de Posgrado, Escuela Superior de Medicina, Instituto Politécnico Nacional, Mexico
| | - David Mendez-Luna
- Laboratorio de modelado Molecular y Diseño de Fármacos, Sección de Estudios de Posgrado, Escuela Superior de Medicina, Instituto Politécnico Nacional, Mexico
| | | | - Carmen Castillo
- Laboratorio de Investigación Integral Cardiometabólica, Sección de Estudios de Posgrado, Escuela Superior de Medicina, Instituto Politécnico Nacional, Mexico
| | - Gustavo Guevara
- Laboratorio de Investigación Integral Cardiometabólica, Sección de Estudios de Posgrado, Escuela Superior de Medicina, Instituto Politécnico Nacional, Mexico
| | - Israel Ramirez-Sanchez
- University of California, San Diego, School of Medicine, 9500 Gilman Drive, La Jolla, CA, USA; Laboratorio de Investigación Integral Cardiometabólica, Sección de Estudios de Posgrado, Escuela Superior de Medicina, Instituto Politécnico Nacional, Mexico
| | - José Correa-Basurto
- Laboratorio de modelado Molecular y Diseño de Fármacos, Sección de Estudios de Posgrado, Escuela Superior de Medicina, Instituto Politécnico Nacional, Mexico; Laboratorio de Investigación Integral Cardiometabólica, Sección de Estudios de Posgrado, Escuela Superior de Medicina, Instituto Politécnico Nacional, Mexico
| | - Guillermo Ceballos
- Laboratorio de Investigación Integral Cardiometabólica, Sección de Estudios de Posgrado, Escuela Superior de Medicina, Instituto Politécnico Nacional, Mexico
| | - Francisco Villarreal
- University of California, San Diego, School of Medicine, 9500 Gilman Drive, La Jolla, CA, USA.
| |
Collapse
|
56
|
Abstract
The growing body of transcriptomic, proteomic, metabolomic and genomic data generated from disease states provides a great opportunity to improve our current understanding of the molecular mechanisms driving diseases and shared between diseases. The use of both clinical and molecular phenotypes will lead to better disease understanding and classification. In this study, we set out to gain novel insights into diseases and their relationships by utilising knowledge gained from system-level molecular data. We integrated different types of biological data including genome-wide association studies data, disease-chemical associations, biological pathways and Gene Ontology annotations into an Integrated Disease Network (IDN), a heterogeneous network where nodes are bio-entities and edges between nodes represent their associations. We also introduced a novel disease similarity measure to infer disease-disease associations from the IDN. Our predicted associations were systemically evaluated against the Medical Subject Heading classification and a statistical measure of disease co-occurrence in PubMed. The strong correlation between our predictions and co-occurrence associations indicated the ability of our approach to recover known disease associations. Furthermore, we presented a case study of Crohn's disease. We demonstrated that our approach not only identified well-established connections between Crohn's disease and other diseases, but also revealed new, interesting connections consistent with emerging literature. Our approach also enabled ready access to the knowledge supporting these new connections, making this a powerful approach for exploring connections between diseases.
Collapse
Affiliation(s)
- Kai Sun
- Department of Computing, Imperial College London, London, SW7 2AZ, UK.
| | | | | | | |
Collapse
|
57
|
Salentin S, Schreiber S, Haupt VJ, Adasme MF, Schroeder M. PLIP: fully automated protein-ligand interaction profiler. Nucleic Acids Res 2015; 43:W443-7. [PMID: 25873628 PMCID: PMC4489249 DOI: 10.1093/nar/gkv315] [Citation(s) in RCA: 1175] [Impact Index Per Article: 130.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2015] [Accepted: 03/28/2015] [Indexed: 11/14/2022] Open
Abstract
The characterization of interactions in protein-ligand complexes is essential for research in structural bioinformatics, drug discovery and biology. However, comprehensive tools are not freely available to the research community. Here, we present the protein-ligand interaction profiler (PLIP), a novel web service for fully automated detection and visualization of relevant non-covalent protein-ligand contacts in 3D structures, freely available at projects.biotec.tu-dresden.de/plip-web. The input is either a Protein Data Bank structure, a protein or ligand name, or a custom protein-ligand complex (e.g. from docking). In contrast to other tools, the rule-based PLIP algorithm does not require any structure preparation. It returns a list of detected interactions on single atom level, covering seven interaction types (hydrogen bonds, hydrophobic contacts, pi-stacking, pi-cation interactions, salt bridges, water bridges and halogen bonds). PLIP stands out by offering publication-ready images, PyMOL session files to generate custom images and parsable result files to facilitate successive data processing. The full python source code is available for download on the website. PLIP's command-line mode allows for high-throughput interaction profiling.
Collapse
Affiliation(s)
- Sebastian Salentin
- Biotechnology Center (BIOTEC), TU Dresden, Tatzberg 47-49, 01307 Dresden, Germany
| | - Sven Schreiber
- Biotechnology Center (BIOTEC), TU Dresden, Tatzberg 47-49, 01307 Dresden, Germany
| | - V Joachim Haupt
- Biotechnology Center (BIOTEC), TU Dresden, Tatzberg 47-49, 01307 Dresden, Germany
| | - Melissa F Adasme
- Biotechnology Center (BIOTEC), TU Dresden, Tatzberg 47-49, 01307 Dresden, Germany Escuela de Ingeniería en Bioinformática, Universidad de Talca, Avda. Lircay s/n Talca, 3460000, Chile
| | - Michael Schroeder
- Biotechnology Center (BIOTEC), TU Dresden, Tatzberg 47-49, 01307 Dresden, Germany
| |
Collapse
|
58
|
Bellera CL, Balcazar DE, Vanrell MC, Casassa AF, Palestro PH, Gavernet L, Labriola CA, Gálvez J, Bruno-Blanch LE, Romano PS, Carrillo C, Talevi A. Computer-guided drug repurposing: Identification of trypanocidal activity of clofazimine, benidipine and saquinavir. Eur J Med Chem 2015; 93:338-48. [DOI: 10.1016/j.ejmech.2015.01.065] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2014] [Revised: 12/29/2014] [Accepted: 01/28/2015] [Indexed: 01/31/2023]
|
59
|
Issa NT, Peters OJ, Byers SW, Dakshanamurthy S. RepurposeVS: A Drug Repurposing-Focused Computational Method for Accurate Drug-Target Signature Predictions. Comb Chem High Throughput Screen 2015; 18:784-94. [PMID: 26234515 PMCID: PMC5848469 DOI: 10.2174/1386207318666150803130138] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2015] [Revised: 06/17/2015] [Accepted: 07/28/2015] [Indexed: 11/22/2022]
Abstract
We describe here RepurposeVS for the reliable prediction of drug-target signatures using X-ray protein crystal structures. RepurposeVS is a virtual screening method that incorporates docking, drug-centric and protein-centric 2D/3D fingerprints with a rigorous mathematical normalization procedure to account for the variability in units and provide high-resolution contextual information for drug-target binding. Validity was confirmed by the following: (1) providing the greatest enrichment of known drug binders for multiple protein targets in virtual screening experiments, (2) determining that similarly shaped protein target pockets are predicted to bind drugs of similar 3D shapes when RepurposeVS is applied to 2,335 human protein targets, and (3) determining true biological associations in vitro for mebendazole (MBZ) across many predicted kinase targets for potential cancer repurposing. Since RepurposeVS is a drug repurposing-focused method, benchmarking was conducted on a set of 3,671 FDA approved and experimental drugs rather than the Database of Useful Decoys (DUDE) so as to streamline downstream repurposing experiments. We further apply RepurposeVS to explore the overall potential drug repurposing space for currently approved drugs. RepurposeVS is not computationally intensive and increases performance accuracy, thus serving as an efficient and powerful in silico tool to predict drug-target associations in drug repurposing.
Collapse
|
60
|
Silvério-Machado R, Couto BRGM, dos Santos MA. Retrieval of Enterobacteriaceae drug targets using singular value decomposition. Bioinformatics 2014; 31:1267-73. [DOI: 10.1093/bioinformatics/btu792] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2014] [Accepted: 11/23/2014] [Indexed: 01/25/2023] Open
|
61
|
Tan F, Yang R, Xu X, Chen X, Wang Y, Ma H, Liu X, Wu X, Chen Y, Liu L, Jia X. Drug repositioning by applying 'expression profiles' generated by integrating chemical structure similarity and gene semantic similarity. MOLECULAR BIOSYSTEMS 2014; 10:1126-38. [PMID: 24603772 DOI: 10.1039/c3mb70554d] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Drug repositioning, also known as drug repurposing or reprofiling, is the process of finding new indications for established drugs. Because drug repositioning can reduce costs and enhance the efficiency of drug development, it is of paramount importance in medical research. Here, we present a systematic computational method to identify potential novel indications for a given drug. This method utilizes some prior knowledge such as 3D drug chemical structure information, drug-target interactions and gene semantic similarity information. Its prediction is based on another form of 'expression profile', which contains scores ranging from -1 to 1, reflecting the consensus response scores (CRSs) between each drug of 965 and 1560 proteins. The CRS integrates chemical structure similarity and gene semantic similarity information. We define the degree of similarity between two drugs as the absolute value of their correlation coefficients. Finally, we establish a drug similarity network (DSN) and obtain 33 modules of drugs with similar modes of action, determining their common indications. Using these modules, we predict new indications for 143 drugs and identify previously unknown indications for 42 drugs without ATC codes. This method overcomes the instability of gene expression profiling derived from experiments due to experimental conditions, and predicts indications for a new compound feasibly, requiring only the 3D structure of the compound. In addition, the high literature validation rate of 71.8% also suggests that our method has the potential to discover novel drug indications for existing drugs.
Collapse
Affiliation(s)
- Fujian Tan
- College of Bioinformatics Science and Technology, Harbin Medical University, 194 Xuefu Road, Harbin, Heilongjiang 150081, PR China.
| | | | | | | | | | | | | | | | | | | | | |
Collapse
|
62
|
Early repositioning through compound set enrichment analysis: a knowledge-recycling strategy. Future Med Chem 2014; 6:563-75. [PMID: 24649958 DOI: 10.4155/fmc.14.4] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Despite famous serendipitous drug repositioning success stories, systematic projects have not yet delivered the expected results. However, repositioning technologies are gaining ground in different phases of routine drug development, together with new adaptive strategies. We demonstrate the power of the compound information pool, the ever-growing heterogeneous information repertoire of approved drugs and candidates as an invaluable catalyzer in this transition. Systematic, computational utilization of this information pool for candidates in early phases is an open research problem; we propose a novel application of the enrichment analysis statistical framework for fusion of this information pool, specifically for the prediction of indications. Pharmaceutical consequences are formulated for a systematic and continuous knowledge recycling strategy, utilizing this information pool throughout the drug-discovery pipeline.
Collapse
|
63
|
Liu R, Singh N, Tawa GJ, Wallqvist A, Reifman J. Exploiting large-scale drug-protein interaction information for computational drug repurposing. BMC Bioinformatics 2014; 15:210. [PMID: 24950817 PMCID: PMC4079911 DOI: 10.1186/1471-2105-15-210] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2014] [Accepted: 06/09/2014] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Despite increased investment in pharmaceutical research and development, fewer and fewer new drugs are entering the marketplace. This has prompted studies in repurposing existing drugs for use against diseases with unmet medical needs. A popular approach is to develop a classification model based on drugs with and without a desired therapeutic effect. For this approach to be statistically sound, it requires a large number of drugs in both classes. However, given few or no approved drugs for the diseases of highest medical urgency and interest, different strategies need to be investigated. RESULTS We developed a computational method termed "drug-protein interaction-based repurposing" (DPIR) that is potentially applicable to diseases with very few approved drugs. The method, based on genome-wide drug-protein interaction information and Bayesian statistics, first identifies drug-protein interactions associated with a desired therapeutic effect. Then, it uses key drug-protein interactions to score other drugs for their potential to have the same therapeutic effect. CONCLUSIONS Detailed cross-validation studies using United States Food and Drug Administration-approved drugs for hypertension, human immunodeficiency virus, and malaria indicated that DPIR provides robust predictions. It achieves high levels of enrichment of drugs approved for a disease even with models developed based on a single drug known to treat the disease. Analysis of our model predictions also indicated that the method is potentially useful for understanding molecular mechanisms of drug action and for identifying protein targets that may potentiate the desired therapeutic effects of other drugs (combination therapies).
Collapse
Affiliation(s)
- Ruifeng Liu
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U,S, Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA.
| | | | | | | | | |
Collapse
|
64
|
Salentin S, Haupt VJ, Daminelli S, Schroeder M. Polypharmacology rescored: protein-ligand interaction profiles for remote binding site similarity assessment. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2014; 116:174-86. [PMID: 24923864 DOI: 10.1016/j.pbiomolbio.2014.05.006] [Citation(s) in RCA: 65] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2014] [Revised: 05/20/2014] [Accepted: 05/26/2014] [Indexed: 11/27/2022]
Abstract
Detection of remote binding site similarity in proteins plays an important role for drug repositioning and off-target effect prediction. Various non-covalent interactions such as hydrogen bonds and van-der-Waals forces drive ligands' molecular recognition by binding sites in proteins. The increasing amount of available structures of protein-small molecule complexes enabled the development of comparative approaches. Several methods have been developed to characterize and compare protein-ligand interaction patterns. Usually implemented as fingerprints, these are mainly used for post processing docking scores and (off-)target prediction. In the latter application, interaction profiles detect similarities in the bound interactions of different ligands and thus identify essential interactions between a protein and its small molecule ligands. Interaction pattern similarity correlates with binding site similarity and is thus contributing to a higher precision in binding site similarity assessment of proteins with distinct global structure. This renders it valuable for existing drug repositioning approaches in structural bioinformatics. Current methods to characterize and compare structure-based interaction patterns - both for protein-small-molecule and protein-protein interactions - as well as their potential in target prediction will be reviewed in this article. The question of how the set of interaction types, flexibility or water-mediated interactions, influence the comparison of interaction patterns will be discussed. Due to the wealth of protein-ligand structures available today, predicted targets can be ranked by comparing their ligand interaction pattern to patterns of the known target. Such knowledge-based methods offer high precision in comparison to methods comparing whole binding sites based on shape and amino acid physicochemical similarity.
Collapse
|
65
|
Konc J, Janežič D. ProBiS-ligands: a web server for prediction of ligands by examination of protein binding sites. Nucleic Acids Res 2014; 42:W215-20. [PMID: 24861616 PMCID: PMC4086080 DOI: 10.1093/nar/gku460] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
The ProBiS-ligands web server predicts binding of ligands to a protein structure. Starting with a protein structure or binding site, ProBiS-ligands first identifies template proteins in the Protein Data Bank that share similar binding sites. Based on the superimpositions of the query protein and the similar binding sites found, the server then transposes the ligand structures from those sites to the query protein. Such ligand prediction supports many activities, e.g. drug repurposing. The ProBiS-ligands web server, an extension of the ProBiS web server, is open and free to all users at http://probis.cmm.ki.si/ligands.
Collapse
Affiliation(s)
- Janez Konc
- National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
| | - Dušanka Janežič
- University of Primorska, Faculty of Mathematics, Natural Sciences and Information Technologies, Glagoljaška 8, 6000 Koper, Slovenia
| |
Collapse
|
66
|
Li L, Wang L, Si Y. Electronic circular dichroism behavior of chiral Phthiobuzone. Acta Pharm Sin B 2014; 4:167-71. [PMID: 26579380 PMCID: PMC4590302 DOI: 10.1016/j.apsb.2014.01.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2013] [Revised: 12/10/2013] [Accepted: 01/07/2014] [Indexed: 11/16/2022] Open
Abstract
Phthiobuzone is a bis(thiosemicarbazone) derivative with a single chiral center which has been used as a racemate in the clinical treatment of herpes and trachoma diseases. In this study, its two enantiomers were prepared from chiral amino acids and their absolute configurations were investigated by electronic circular dichroism (ECD) combined with modern quantum-chemical calculations using time-dependent density functional theory. It was found that solvation changed both the conformational distribution and the ECD spectrum of each conformer. The theoretical ECD spectra of the two enantiomers were in good agreement with the experimentally determined spectra of the corresponding isomers in dimethyl sulfoxide. The ECD behavior of the bis(thiosemicarbazone) chromophore in a chiral environment is also discussed. Our results indicate that ECD spectroscopy may be a useful tool for the stereochemical evaluation of chiral drugs.
Collapse
|
67
|
Croset S, Overington JP, Rebholz-Schuhmann D. The functional therapeutic chemical classification system. Bioinformatics 2014; 30:876-83. [PMID: 24177719 PMCID: PMC3957075 DOI: 10.1093/bioinformatics/btt628] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2013] [Revised: 10/15/2013] [Accepted: 10/27/2013] [Indexed: 01/27/2023] Open
Abstract
MOTIVATION Drug repositioning is the discovery of new indications for compounds that have already been approved and used in a clinical setting. Recently, some computational approaches have been suggested to unveil new opportunities in a systematic fashion, by taking into consideration gene expression signatures or chemical features for instance. We present here a novel method based on knowledge integration using semantic technologies, to capture the functional role of approved chemical compounds. RESULTS In order to computationally generate repositioning hypotheses, we used the Web Ontology Language to formally define the semantics of over 20 000 terms with axioms to correctly denote various modes of action (MoA). Based on an integration of public data, we have automatically assigned over a thousand of approved drugs into these MoA categories. The resulting new resource is called the Functional Therapeutic Chemical Classification System and was further evaluated against the content of the traditional Anatomical Therapeutic Chemical Classification System. We illustrate how the new classification can be used to generate drug repurposing hypotheses, using Alzheimers disease as a use-case. AVAILABILITY https://www.ebi.ac.uk/chembl/ftc; https://github.com/loopasam/ftc. CONTACT croset@ebi.ac.uk SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Samuel Croset
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | | | | |
Collapse
|
68
|
Engin HB, Guney E, Keskin O, Oliva B, Gursoy A. Integrating structure to protein-protein interaction networks that drive metastasis to brain and lung in breast cancer. PLoS One 2013; 8:e81035. [PMID: 24278371 PMCID: PMC3838352 DOI: 10.1371/journal.pone.0081035] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2013] [Accepted: 10/05/2013] [Indexed: 11/18/2022] Open
Abstract
Blocking specific protein interactions can lead to human diseases. Accordingly, protein interactions and the structural knowledge on interacting surfaces of proteins (interfaces) have an important role in predicting the genotype-phenotype relationship. We have built the phenotype specific sub-networks of protein-protein interactions (PPIs) involving the relevant genes responsible for lung and brain metastasis from primary tumor in breast cancer. First, we selected the PPIs most relevant to metastasis causing genes (seed genes), by using the "guilt-by-association" principle. Then, we modeled structures of the interactions whose complex forms are not available in Protein Databank (PDB). Finally, we mapped mutations to interface structures (real and modeled), in order to spot the interactions that might be manipulated by these mutations. Functional analyses performed on these sub-networks revealed the potential relationship between immune system-infectious diseases and lung metastasis progression, but this connection was not observed significantly in the brain metastasis. Besides, structural analyses showed that some PPI interfaces in both metastasis sub-networks are originating from microbial proteins, which in turn were mostly related with cell adhesion. Cell adhesion is a key mechanism in metastasis, therefore these PPIs may be involved in similar molecular pathways that are shared by infectious disease and metastasis. Finally, by mapping the mutations and amino acid variations on the interface regions of the proteins in the metastasis sub-networks we found evidence for some mutations to be involved in the mechanisms differentiating the type of the metastasis.
Collapse
Affiliation(s)
- H. Billur Engin
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, Istanbul, Turkey
| | - Emre Guney
- Structural Bioinformatics Group (GRIB), Universitat Pompeu Fabra
| | - Ozlem Keskin
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, Istanbul, Turkey
| | - Baldo Oliva
- Structural Bioinformatics Group (GRIB), Universitat Pompeu Fabra
| | - Attila Gursoy
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, Istanbul, Turkey
| |
Collapse
|
69
|
Jalencas X, Mestres J. Identification of Similar Binding Sites to Detect Distant Polypharmacology. Mol Inform 2013; 32:976-90. [PMID: 27481143 DOI: 10.1002/minf.201300082] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2013] [Accepted: 07/29/2013] [Indexed: 01/19/2023]
Abstract
The ability of small molecules to interact with multiple proteins is referred to as polypharmacology. This property is often linked to the therapeutic action of drugs but it is known also to be responsible for many of their side effects. Because of its importance, the development of computational methods that can predict drug polypharmacology has become an important line of research that led recently to the identification of many novel targets for known drugs. Nowadays, the majority of these methods are based on measuring the similarity of a query molecule against the hundreds of thousands of molecules for which pharmacological data on thousands of proteins are available in public sources. However, similarity-based methods are inherently biased by the chemical coverage offered by the active molecules present in those public repositories, which limits significantly their capacity to predict interactions with proteins structurally and functionally unrelated to any of the already known targets for drugs. It is in this respect that structure-based methods aiming at identifying similar binding sites may offer an alternative complementary means to ligand-based methods for detecting distant polypharmacology. The different existing approaches to binding site detection, representation, comparison, and fragmentation are reviewed and recent successful applications presented.
Collapse
Affiliation(s)
- Xavier Jalencas
- Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Research Institute & University Pompeu Fabra, Parc de Recerca Biomèdica, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain fax: +34 93 3160550
| | - Jordi Mestres
- Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Research Institute & University Pompeu Fabra, Parc de Recerca Biomèdica, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain fax: +34 93 3160550.
| |
Collapse
|
70
|
Gfeller D, Michielin O, Zoete V. Shaping the interaction landscape of bioactive molecules. ACTA ACUST UNITED AC 2013; 29:3073-9. [PMID: 24048355 DOI: 10.1093/bioinformatics/btt540] [Citation(s) in RCA: 275] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
MOTIVATION Most bioactive molecules perform their action by interacting with proteins or other macromolecules. However, for a significant fraction of them, the primary target remains unknown. In addition, the majority of bioactive molecules have more than one target, many of which are poorly characterized. Computational predictions of bioactive molecule targets based on similarity with known ligands are powerful to narrow down the number of potential targets and to rationalize side effects of known molecules. RESULTS Using a reference set of 224 412 molecules active on 1700 human proteins, we show that accurate target prediction can be achieved by combining different measures of chemical similarity based on both chemical structure and molecular shape. Our results indicate that the combined approach is especially efficient when no ligand with the same scaffold or from the same chemical series has yet been discovered. We also observe that different combinations of similarity measures are optimal for different molecular properties, such as the number of heavy atoms. This further highlights the importance of considering different classes of similarity measures between new molecules and known ligands to accurately predict their targets.
Collapse
Affiliation(s)
- David Gfeller
- Swiss Institute of Bioinformatics (SIB), Quartier Sorge, Bâtiment Génopode, CH-1015 Lausanne, Switzerland, Ludwig Institute for Cancer Research and Pluridisciplinary Center for Clinical Oncology, Centre Hospitalier Universitaire Vaudois, CH-1015 Lausanne, Switzerland
| | | | | |
Collapse
|
71
|
Haupt VJ, Daminelli S, Schroeder M. Drug Promiscuity in PDB: Protein Binding Site Similarity Is Key. PLoS One 2013; 8:e65894. [PMID: 23805191 PMCID: PMC3689763 DOI: 10.1371/journal.pone.0065894] [Citation(s) in RCA: 107] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2012] [Accepted: 04/30/2013] [Indexed: 11/19/2022] Open
Abstract
Drug repositioning applies established drugs to new disease indications with increasing success. A pre-requisite for drug repurposing is drug promiscuity (polypharmacology) – a drug’s ability to bind to several targets. There is a long standing debate on the reasons for drug promiscuity. Based on large compound screens, hydrophobicity and molecular weight have been suggested as key reasons. However, the results are sometimes contradictory and leave space for further analysis. Protein structures offer a structural dimension to explain promiscuity: Can a drug bind multiple targets because the drug is flexible or because the targets are structurally similar or even share similar binding sites? We present a systematic study of drug promiscuity based on structural data of PDB target proteins with a set of 164 promiscuous drugs. We show that there is no correlation between the degree of promiscuity and ligand properties such as hydrophobicity or molecular weight but a weak correlation to conformational flexibility. However, we do find a correlation between promiscuity and structural similarity as well as binding site similarity of protein targets. In particular, 71% of the drugs have at least two targets with similar binding sites. In order to overcome issues in detection of remotely similar binding sites, we employed a score for binding site similarity: LigandRMSD measures the similarity of the aligned ligands and uncovers remote local similarities in proteins. It can be applied to arbitrary structural binding site alignments. Three representative examples, namely the anti-cancer drug methotrexate, the natural product quercetin and the anti-diabetic drug acarbose are discussed in detail. Our findings suggest that global structural and binding site similarity play a more important role to explain the observed drug promiscuity in the PDB than physicochemical drug properties like hydrophobicity or molecular weight. Additionally, we find ligand flexibility to have a minor influence.
Collapse
Affiliation(s)
| | | | - Michael Schroeder
- Biotechnology Center (BIOTEC), TU Dresden, Dresden, Germany
- * E-mail:
| |
Collapse
|
72
|
Schenone M, Dančík V, Wagner BK, Clemons PA. Target identification and mechanism of action in chemical biology and drug discovery. Nat Chem Biol 2013; 9:232-40. [PMID: 23508189 PMCID: PMC5543995 DOI: 10.1038/nchembio.1199] [Citation(s) in RCA: 628] [Impact Index Per Article: 57.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2012] [Accepted: 01/28/2013] [Indexed: 12/12/2022]
Abstract
Target-identification and mechanism-of-action studies have important roles in small-molecule probe and drug discovery. Biological and technological advances have resulted in the increasing use of cell-based assays to discover new biologically active small molecules. Such studies allow small-molecule action to be tested in a more disease-relevant setting at the outset, but they require follow-up studies to determine the precise protein target or targets responsible for the observed phenotype. Target identification can be approached by direct biochemical methods, genetic interactions or computational inference. In many cases, however, combinations of approaches may be required to fully characterize on-target and off-target effects and to understand mechanisms of small-molecule action.
Collapse
Affiliation(s)
- Monica Schenone
- Proteomics Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Vlado Dančík
- Chemical Biology Program, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Bridget K Wagner
- Chemical Biology Program, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Paul A Clemons
- Chemical Biology Program, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| |
Collapse
|
73
|
Mei JP, Kwoh CK, Yang P, Li XL, Zheng J. Drug-target interaction prediction by learning from local information and neighbors. ACTA ACUST UNITED AC 2012; 29:238-45. [PMID: 23162055 DOI: 10.1093/bioinformatics/bts670] [Citation(s) in RCA: 212] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
MOTIVATION In silico methods provide efficient ways to predict possible interactions between drugs and targets. Supervised learning approach, bipartite local model (BLM), has recently been shown to be effective in prediction of drug-target interactions. However, for drug-candidate compounds or target-candidate proteins that currently have no known interactions available, its pure 'local' model is not able to be learned and hence BLM may fail to make correct prediction when involving such kind of new candidates. RESULTS We present a simple procedure called neighbor-based interaction-profile inferring (NII) and integrate it into the existing BLM method to handle the new candidate problem. Specifically, the inferred interaction profile is treated as label information and is used for model learning of new candidates. This functionality is particularly important in practice to find targets for new drug-candidate compounds and identify targeting drugs for new target-candidate proteins. Consistent good performance of the new BLM-NII approach has been observed in the experiment for the prediction of interactions between drugs and four categories of target proteins. Especially for nuclear receptors, BLM-NII achieves the most significant improvement as this dataset contains many drugs/targets with no interactions in the cross-validation. This demonstrates the effectiveness of the NII strategy and also shows the great potential of BLM-NII for prediction of compound-protein interactions. CONTACT jpmei@ntu.edu.sg SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Jian-Ping Mei
- Bioinformatics Research Centre, School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798.
| | | | | | | | | |
Collapse
|
74
|
Affiliation(s)
- Nagasuma Chandra
- Indian Institute of Science, Department of Biochemistry,
Bangalore – 560012, India ,
| | - Jyothi Padiadpu
- Indian Institute of Science, Department of Biochemistry,
Bangalore – 560012, India
| |
Collapse
|
75
|
Lecca P, Priami C. Biological network inference for drug discovery. Drug Discov Today 2012; 18:256-64. [PMID: 23147668 DOI: 10.1016/j.drudis.2012.11.001] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2012] [Revised: 10/04/2012] [Accepted: 11/05/2012] [Indexed: 12/31/2022]
Abstract
A better understanding of the pathophysiology should help deliver drugs whose targets are involved in the causative processes underlying a disease. Biological network inference uses computational methods for deducing from high-throughput experimental data, the topology and the causal structure of the interactions among the drugs and their targets. Therefore, biological network inference can support and contribute to the experimental identification of both gene and protein networks causing a disease as well as the biochemical networks of drugs metabolism and mechanisms of action. The resulting high-level networks serve as a foundational basis for more detailed mechanistic models and are increasingly used in drug discovery by pharmaceutical and biotechnology companies. We review and compare recent computational technologies for network inference applied to drug discovery.
Collapse
Affiliation(s)
- Paola Lecca
- The Microsoft Research, University of Trento, Centre for Computational and Systems Biology, Piazza Manifattura 1 - 38068 Rovereto, Italy.
| | | |
Collapse
|
76
|
Zeng LF, Wang Y, Kazemi R, Xu S, Xu ZL, Sanchez TW, Yang LM, Debnath B, Odde S, Xie H, Zheng YT, Ding J, Neamati N, Long YQ. Repositioning HIV-1 Integrase Inhibitors for Cancer Therapeutics: 1,6-Naphthyridine-7-carboxamide as a Promising Scaffold with Drug-like Properties. J Med Chem 2012; 55:9492-509. [DOI: 10.1021/jm300667v] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Li-Fan Zeng
- State Key Laboratory of Drug Research, Shanghai Institute of Materia
Medica, Shanghai Institutes for Biological Sciences, Chinese Academy
of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
| | - Yong Wang
- State Key Laboratory of Drug Research, Shanghai Institute of Materia
Medica, Shanghai Institutes for Biological Sciences, Chinese Academy
of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
| | - Roza Kazemi
- Department of Pharmacology and Pharmaceutical
Sciences, School of Pharmacy, University of Southern California, 1985
Zonal Avenue, Los Angeles, California 90089, United States
| | - Shili Xu
- Department of Pharmacology and Pharmaceutical
Sciences, School of Pharmacy, University of Southern California, 1985
Zonal Avenue, Los Angeles, California 90089, United States
| | - Zhong-Liang Xu
- State Key Laboratory of Drug Research, Shanghai Institute of Materia
Medica, Shanghai Institutes for Biological Sciences, Chinese Academy
of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
| | - Tino W. Sanchez
- Department of Pharmacology and Pharmaceutical
Sciences, School of Pharmacy, University of Southern California, 1985
Zonal Avenue, Los Angeles, California 90089, United States
| | - Liu-Meng Yang
- Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences and Yunnan Province, Kunming
Institute of Zoology, Chinese Academy of Sciences, Kunming 650223,
China
| | - Bikash Debnath
- Department of Pharmacology and Pharmaceutical
Sciences, School of Pharmacy, University of Southern California, 1985
Zonal Avenue, Los Angeles, California 90089, United States
| | - Srinivas Odde
- Department of Pharmacology and Pharmaceutical
Sciences, School of Pharmacy, University of Southern California, 1985
Zonal Avenue, Los Angeles, California 90089, United States
| | - Hua Xie
- State Key Laboratory of Drug Research, Shanghai Institute of Materia
Medica, Shanghai Institutes for Biological Sciences, Chinese Academy
of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
| | - Yong-Tang Zheng
- Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences and Yunnan Province, Kunming
Institute of Zoology, Chinese Academy of Sciences, Kunming 650223,
China
| | - Jian Ding
- State Key Laboratory of Drug Research, Shanghai Institute of Materia
Medica, Shanghai Institutes for Biological Sciences, Chinese Academy
of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
| | - Nouri Neamati
- Department of Pharmacology and Pharmaceutical
Sciences, School of Pharmacy, University of Southern California, 1985
Zonal Avenue, Los Angeles, California 90089, United States
| | - Ya-Qiu Long
- State Key Laboratory of Drug Research, Shanghai Institute of Materia
Medica, Shanghai Institutes for Biological Sciences, Chinese Academy
of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
| |
Collapse
|
77
|
Mei H, Xia T, Feng G, Zhu J, Lin SM, Qiu Y. Opportunities in systems biology to discover mechanisms and repurpose drugs for CNS diseases. Drug Discov Today 2012; 17:1208-16. [DOI: 10.1016/j.drudis.2012.06.015] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2012] [Revised: 06/04/2012] [Accepted: 06/25/2012] [Indexed: 01/07/2023]
|
78
|
Brüning A. Targeting the off-targets: a computational bioinformatics approach to understanding the polypharmacology of nelfinavir. Expert Rev Clin Pharmacol 2012; 4:571-3. [PMID: 22114885 DOI: 10.1586/ecp.11.37] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In recent years, the identification of new pharmacological effects of already established or abandoned drugs has become a valuable tool for drug repositioning purposes. The HIV drug nelfinavir belongs to those drugs for which empirical data indicate additional pharmacological applications for various diseases, including cancer. To identify and confirm binding partners of nelfinavir other than HIV-1 protease, Xie et al. performed a systematic computational analysis to identify possible structural similarities between the nelfinavir-binding pocket of HIV-1 protease and 5985 protein database entries. Of 126 possible binding partners to nelfinavir, a remarkably high percentage of protein kinases were identified. Further in-depth computational ligand-binding studies indicated the EGF receptor and cytosolic protein kinase B as the most likely off-targets of nelfinavir. Astonishingly, these in silico data are in accordance with previous data obtained by experimental in vitro analysis, indicating a high predictive value of the computer-based approach developed and applied by Xie et al. The computational approach and the authors' results, with respect to their integration in systems biology, are presented and discussed.
Collapse
Affiliation(s)
- Ansgar Brüning
- University Hospital Munich, Department of Obstetrics/Gynecology, Molecular Biology Laboratory, Maistrasse 11, 80337 München, Germany.
| |
Collapse
|
79
|
Engin HB, Keskin O, Nussinov R, Gursoy A. A strategy based on protein-protein interface motifs may help in identifying drug off-targets. J Chem Inf Model 2012; 52:2273-86. [PMID: 22817115 PMCID: PMC3979525 DOI: 10.1021/ci300072q] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Networks are increasingly used to study the impact of drugs at the systems level. From the algorithmic standpoint, a drug can "attack" nodes or edges of a protein-protein interaction network. In this work, we propose a new network strategy, "The Interface Attack", based on protein-protein interfaces. Similar interface architectures can occur between unrelated proteins. Consequently, in principle, a drug that binds to one has a certain probability of binding to others. The interface attack strategy simultaneously removes from the network all interactions that consist of similar interface motifs. This strategy is inspired by network pharmacology and allows inferring potential off-targets. We introduce a network model that we call "Protein Interface and Interaction Network (P2IN)", which is the integration of protein-protein interface structures and protein interaction networks. This interface-based network organization clarifies which protein pairs have structurally similar interfaces and which proteins may compete to bind the same surface region. We built the P2IN with the p53 signaling network and performed network robustness analysis. We show that (1) "hitting" frequent interfaces (a set of edges distributed around the network) might be as destructive as eleminating high degree proteins (hub nodes), (2) frequent interfaces are not always topologically critical elements in the network, and (3) interface attack may reveal functional changes in the system better than the attack of single proteins. In the off-target detection case study, we found that drugs blocking the interface between CDK6 and CDKN2D may also affect the interaction between CDK4 and CDKN2D.
Collapse
Affiliation(s)
- H. Billur Engin
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, Rumelifeneri Yolu, 34450 Sariyer Istanbul, Turkey
| | - Ozlem Keskin
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, Rumelifeneri Yolu, 34450 Sariyer Istanbul, Turkey
| | - Ruth Nussinov
- Center for Cancer Research Nanobiology Program, NCI-Frederick, Frederick, MD 21702
- Sackler Inst. Of Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Attila Gursoy
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, Rumelifeneri Yolu, 34450 Sariyer Istanbul, Turkey
| |
Collapse
|
80
|
Wildsmith JAW. Continuous thoracic epidural block for surgery: gold standard or debased currency? Br J Anaesth 2012; 109:9-12. [PMID: 22696554 DOI: 10.1093/bja/aes177] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
|
81
|
Iorio F, Rittman T, Ge H, Menden M, Saez-Rodriguez J. Transcriptional data: a new gateway to drug repositioning? Drug Discov Today 2012; 18:350-7. [PMID: 22897878 PMCID: PMC3625109 DOI: 10.1016/j.drudis.2012.07.014] [Citation(s) in RCA: 153] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2012] [Revised: 07/19/2012] [Accepted: 07/26/2012] [Indexed: 11/26/2022]
Abstract
Recent advances in computational biology suggest that any perturbation to the transcriptional programme of the cell can be summarised by a proper ‘signature’: a set of genes combined with a pattern of expression. Therefore, it should be possible to generate proxies of clinicopathological phenotypes and drug effects through signatures acquired via DNA microarray technology. Gene expression signatures have recently been assembled and compared through genome-wide metrics, unveiling unexpected drug–disease and drug–drug ‘connections’ by matching corresponding signatures. Consequently, novel applications for existing drugs have been predicted and experimentally validated. Here, we describe related methods, case studies and resources while discussing challenges and benefits of exploiting existing repositories of microarray data that could serve as a search space for systematic drug repositioning.
Collapse
Affiliation(s)
- Francesco Iorio
- EMBL – European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK
- Cancer Genome Project, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK
| | - Timothy Rittman
- Dept of Clinical Neurosciences, Herchel Smith Building, Forvie Site, Addenbrooke's Hospital, Robinson Way, Cambridge CB2 0SZ, UK
| | - Hong Ge
- Dept of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, Wilberforce Road, Cambridge CB3 0WA, UK
| | - Michael Menden
- EMBL – European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK
| | - Julio Saez-Rodriguez
- EMBL – European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK
- Corresponding author:.
| |
Collapse
|
82
|
Konc J, Depolli M, Trobec R, Rozman K, Janežič D. Parallel-ProBiS: fast parallel algorithm for local structural comparison of protein structures and binding sites. J Comput Chem 2012; 33:2199-203. [PMID: 22718529 DOI: 10.1002/jcc.23048] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2012] [Revised: 05/10/2012] [Accepted: 05/25/2012] [Indexed: 11/12/2022]
Abstract
The ProBiS algorithm performs a local structural comparison of the query protein surface against the nonredundant database of protein structures. It finds proteins that have binding sites in common with the query protein. Here, we present a new parallelized algorithm, Parallel-ProBiS, for detecting similar binding sites on clusters of computers. The obtained speedups of the parallel ProBiS scale almost ideally with the number of computing cores up to about 64 computing cores. Scaling is better for larger than for smaller query proteins. For a protein with almost 600 amino acids, the maximum speedup of 180 was achieved on two interconnected clusters with 248 computing cores. Source code of Parallel-ProBiS is available for download free for academic users at http://probis.cmm.ki.si/download.
Collapse
Affiliation(s)
- Janez Konc
- Laboratory for Molecular Modeling, National Institute of Chemistry, Hajdrihova 19, SI-1000, Ljubljana, Slovenia
| | | | | | | | | |
Collapse
|
83
|
Konc J, Janezic D. ProBiS-2012: web server and web services for detection of structurally similar binding sites in proteins. Nucleic Acids Res 2012; 40:W214-21. [PMID: 22600737 PMCID: PMC3394329 DOI: 10.1093/nar/gks435] [Citation(s) in RCA: 72] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The ProBiS web server is a web server for detection of structurally similar binding sites in the PDB and for local pairwise alignment of protein structures. In this article, we present a new version of the ProBiS web server that is 10 times faster than earlier versions, due to the efficient parallelization of the ProBiS algorithm, which now allows significantly faster comparison of a protein query against the PDB and reduces the calculation time for scanning the entire PDB from hours to minutes. It also features new web services, and an improved user interface. In addition, the new web server is united with the ProBiS-Database and thus provides instant access to pre-calculated protein similarity profiles for over 29 000 non-redundant protein structures. The ProBiS web server is particularly adept at detection of secondary binding sites in proteins. It is freely available at http://probis.cmm.ki.si/old-version, and the new ProBiS web server is at http://probis.cmm.ki.si.
Collapse
Affiliation(s)
- Janez Konc
- National Institute of Chemistry, Ljubljana, Slovenia
| | | |
Collapse
|
84
|
Raval A, Piana S, Eastwood MP, Dror RO, Shaw DE. Refinement of protein structure homology models via long, all-atom molecular dynamics simulations. Proteins 2012; 80:2071-9. [PMID: 22513870 DOI: 10.1002/prot.24098] [Citation(s) in RCA: 184] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2012] [Revised: 04/03/2012] [Accepted: 04/11/2012] [Indexed: 11/07/2022]
Abstract
Accurate computational prediction of protein structure represents a longstanding challenge in molecular biology and structure-based drug design. Although homology modeling techniques are widely used to produce low-resolution models, refining these models to high resolution has proven difficult. With long enough simulations and sufficiently accurate force fields, molecular dynamics (MD) simulations should in principle allow such refinement, but efforts to refine homology models using MD have for the most part yielded disappointing results. It has thus far been unclear whether MD-based refinement is limited primarily by accessible simulation timescales, force field accuracy, or both. Here, we examine MD as a technique for homology model refinement using all-atom simulations, each at least 100 μs long-more than 100 times longer than previous refinement simulations-and a physics-based force field that was recently shown to successfully fold a structurally diverse set of fast-folding proteins. In MD simulations of 24 proteins chosen from the refinement category of recent Critical Assessment of Structure Prediction (CASP) experiments, we find that in most cases, simulations initiated from homology models drift away from the native structure. Comparison with simulations initiated from the native structure suggests that force field accuracy is the primary factor limiting MD-based refinement. This problem can be mitigated to some extent by restricting sampling to the neighborhood of the initial model, leading to structural improvement that, while limited, is roughly comparable to the leading alternative methods.
Collapse
Affiliation(s)
- Alpan Raval
- D E Shaw Research, New York, New York 10036, USA
| | | | | | | | | |
Collapse
|
85
|
Lahti JL, Tang GW, Capriotti E, Liu T, Altman RB. Bioinformatics and variability in drug response: a protein structural perspective. J R Soc Interface 2012; 9:1409-37. [PMID: 22552919 DOI: 10.1098/rsif.2011.0843] [Citation(s) in RCA: 57] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Marketed drugs frequently perform worse in clinical practice than in the clinical trials on which their approval is based. Many therapeutic compounds are ineffective for a large subpopulation of patients to whom they are prescribed; worse, a significant fraction of patients experience adverse effects more severe than anticipated. The unacceptable risk-benefit profile for many drugs mandates a paradigm shift towards personalized medicine. However, prior to adoption of patient-specific approaches, it is useful to understand the molecular details underlying variable drug response among diverse patient populations. Over the past decade, progress in structural genomics led to an explosion of available three-dimensional structures of drug target proteins while efforts in pharmacogenetics offered insights into polymorphisms correlated with differential therapeutic outcomes. Together these advances provide the opportunity to examine how altered protein structures arising from genetic differences affect protein-drug interactions and, ultimately, drug response. In this review, we first summarize structural characteristics of protein targets and common mechanisms of drug interactions. Next, we describe the impact of coding mutations on protein structures and drug response. Finally, we highlight tools for analysing protein structures and protein-drug interactions and discuss their application for understanding altered drug responses associated with protein structural variants.
Collapse
Affiliation(s)
- Jennifer L Lahti
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | | | | | | | | |
Collapse
|
86
|
Abstract
Human diseases can be caused by complex mechanisms involving aberrations in numerous proteins and pathways. With recent advances in genomics, elucidating the molecular basis of disease on a personalized level has become an attainable goal. In many cases, relevant molecular targets will be identified for which approved drugs already exist, and the potential repositioning of these drugs to a new indication can be investigated. Repositioning is an accelerated route for drug discovery because existing drugs have established clinical and pharmacokinetic data. Personalized medicine and repositioning both aim to improve the productivity of current drug discovery pipelines, which expend enormous time and cost to develop new drugs, only to have them fail in clinical trials because of lack of efficacy or toxicity. Here, we discuss the current state of research in these two fields, focusing on recent large-scale efforts to systematically find repositioning candidates and elucidate individual disease mechanisms in cancer. We also discuss scenarios in which personalized drug repositioning could be particularly rewarding, such as for diseases that are rare or have specific mutations, as well as current challenges in this field. With an increasing number of drugs being approved for rare cancer subtypes, personalized medicine and repositioning approaches are poised to significantly alter the way we diagnose diseases, infer treatments and develop new drugs.
Collapse
Affiliation(s)
- Yvonne Y Li
- Canada's Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, British Columbia V5Z 4S6, Canada
| | - Steven Jm Jones
- Canada's Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, British Columbia V5Z 4S6, Canada
| |
Collapse
|
87
|
Konc J, Cesnik T, Konc JT, Penca M, Janežič D. ProBiS-database: precalculated binding site similarities and local pairwise alignments of PDB structures. J Chem Inf Model 2012; 52:604-12. [PMID: 22268964 PMCID: PMC3287116 DOI: 10.1021/ci2005687] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
![]()
ProBiS-Database is a searchable repository of precalculated
local
structural alignments in proteins detected by the ProBiS algorithm
in the Protein Data Bank. Identification of functionally important
binding regions of the protein is facilitated by structural similarity
scores mapped to the query protein structure. PDB structures that
have been aligned with a query protein may be rapidly retrieved from
the ProBiS-Database, which is thus able to generate hypotheses concerning
the roles of uncharacterized proteins. Presented with uncharacterized
protein structure, ProBiS-Database can discern relationships between
such a query protein and other better known proteins in the PDB. Fast
access and a user-friendly graphical interface promote easy exploration
of this database of over 420 million local structural alignments.
The ProBiS-Database is updated weekly and is freely available online
at http://probis.cmm.ki.si/database.
Collapse
Affiliation(s)
- Janez Konc
- National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
| | | | | | | | | |
Collapse
|
88
|
Sanders MPA, McGuire R, Roumen L, de Esch IJP, de Vlieg J, Klomp JPG, de Graaf C. From the protein's perspective: the benefits and challenges of protein structure-based pharmacophore modeling. MEDCHEMCOMM 2012. [DOI: 10.1039/c1md00210d] [Citation(s) in RCA: 59] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Protein structure-based pharmacophore (SBP) models derive the molecular features a ligand must contain to be biologically active by conversion of protein properties to reciprocal ligand space. SBPs improve molecular understanding of ligand–protein interactions and can be used as valuable tools for hit and lead optimization, compound library design, and target hopping.
Collapse
Affiliation(s)
- Marijn P. A. Sanders
- Computational Drug Discovery Group
- CMBI
- Radboud University Nijmegen
- Nijmegen
- The Netherlands
| | | | - Luc Roumen
- Division of Medicinal Chemistry
- LACDR
- VU University Amsterdam
- Amsterdam
- The Netherlands
| | - Iwan J. P. de Esch
- Division of Medicinal Chemistry
- LACDR
- VU University Amsterdam
- Amsterdam
- The Netherlands
| | - Jacob de Vlieg
- Computational Drug Discovery Group
- CMBI
- Radboud University Nijmegen
- Nijmegen
- The Netherlands
| | | | - Chris de Graaf
- Division of Medicinal Chemistry
- LACDR
- VU University Amsterdam
- Amsterdam
- The Netherlands
| |
Collapse
|
89
|
Daminelli S, Haupt VJ, Reimann M, Schroeder M. Drug repositioning through incomplete bi-cliques in an integrated drug–target–disease network. Integr Biol (Camb) 2012; 4:778-88. [DOI: 10.1039/c2ib00154c] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
|
90
|
Iskar M, Zeller G, Zhao XM, van Noort V, Bork P. Drug discovery in the age of systems biology: the rise of computational approaches for data integration. Curr Opin Biotechnol 2011; 23:609-16. [PMID: 22153034 DOI: 10.1016/j.copbio.2011.11.010] [Citation(s) in RCA: 55] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2011] [Accepted: 11/06/2011] [Indexed: 01/08/2023]
Abstract
The increased availability of large-scale open-access resources on bioactivities of small molecules has a significant impact on pharmacology facilitated mainly by computational approaches that digest the vast amounts of data. We discuss here how computational data integration enables systemic views on a drug's action and allows to tackle complex problems such as the large-scale prediction of drug targets, drug repurposing, the molecular mechanisms, cellular responses or side effects. We particularly focus on computational methods that leverage various cell-based transcriptional, proteomic and phenotypic profiles of drug response in order to gain a systemic view of drug action at the molecular, cellular and whole-organism scale.
Collapse
Affiliation(s)
- Murat Iskar
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | | | | | | | | |
Collapse
|
91
|
|